While FPGAs have seen prior use in database systems, in recent years interest in using
FPGA to accelerate databases has declined in both industry and academia for the following …

Vector search has emerged as the foundation for large-scale information retrieval and
machine learning systems, with search engines like Google and Bing processing tens of …

The importance of Sparse Matrix dense Vector multiplication (SpMV) operation in graph
analytics and numerous scientific applications has led to development of custom …

With the emerging big data applications of Machine Learning, Speech Recognition, Artificial
Intelligence, and DNA Sequencing in recent years, computer architecture research …

Embedding-based retrieval (EBR) is widely used in recommendation systems to retrieve
thousands of relevant candidates from a large corpus with millions or more items. A good …

Large-scale sorting is always an important yet demanding task for data center applications.
In addition to powerful processing capability, high-performance sorting system requires …

State-of-the-art studies show that FPGA-based hardware merge sorters (HMSs) can achieve
superior performance compared with optimized algorithms on CPUs and GPUs. The …

The emergence of high-bandwidth memory (HBM) brings new opportunities to boost the
performance of sorting acceleration on FPGAs, which was conventionally bounded by the …

Nonbinary polar codes defined over Galois field GF (q) have shown improved error-
correction performance than binary polar codes using successive-cancellation list (SCL) …

This paper proposes a novel hardware-based multidimensional sorting algorithm and its
respective architecture, called real-time hardware sorter (RTHS), for emerging data intensive …